Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1160132

Photon enhanced thermionic emission

Schwede, Jared·Melosh, Nicholas·Shen, Zhixun
View original source ↗Read source document ↗Export metadata ↓

Abstract

Photon Enhanced Thermionic Emission (PETE) is exploited to provide improved efficiency for radiant energy conversion. A hot (greater than 200.degree. C.) semiconductor cathode is illuminated such that it emits electrons. Because the cathode is hot, significantly more electrons are emitted than would be emitted from a room temperature (or colder) cathode under the same illumination conditions. As a result of this increased electron emission, the energy conversion efficiency can be significantly increased relative to a conventional photovoltaic device. In PETE, the cathode electrons can be (and typically are) thermalized with respect to the cathode. As a result, PETE does not rely on emission of non-thermalized electrons, and is significantly easier to implement than hot-carrier emission approaches.

Explore related subjects

42 ENGINEERING

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Schwede, Jared, Melosh, Nicholas, Shen, Zhixun. 2014-10-07. Photon enhanced thermionic emission. https://www.osti.gov/biblio/1160132

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗